Low-resource Languages: A Review of Past Work and Future Challenges

The lack or absence of parallel and comparable corpora makes bilingual lexicon extraction a difficult task for low-resource languages.

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In recent years, artificial intelligence (AI) has advanced significantly in natural language processing (NLP), marked by powerful pre-trained language models from major companies.

Low-resource languages serve as invaluable repositories of human history, embodying cultural evolution and intellectual diversity.

Natural language processing (NLP) has significantly advanced our ability to model and interact with human language through technology.

There is a lack of multilingual data to support applications in a large number of languages, especially for low-resource languages.

This paper presents an exploration of low resource languages and the specific challenges that arise in natural language understanding of these by a voice assistant.

Proficiency in English is pivotal for leveraging information and communication technologies, but it holds even greater significance in the realm of generative artificial intelligence (GAI), which is poised as the next digital frontier.

Despite the recent advancements of attention-based deep learning architectures across a majority of Natural Language Processing tasks, their application remains limited in a low-resource setting because of a lack of pre-trained models for such languages.

Large Language Models (LLMs) have demonstrated remarkable success across a wide range of tasks and domains.

Text classification is an area of NLP in which major improvements have been observed in recent years, primarily via pre-training and fine-tuning of large language models (LLMs).